Fast and Accurate Multi-Tissue Deconvolution Using SHORE and H-psd Tensors

Abstract

We propose a new regularization for spherical deconvolution in diffusion MRI. It is based on observing that higher-order tensor representations of fiber ODFs should be H-psd, i.e., they should have a positive semidefinite (psd) matrix HT. We show that this constraint is stricter than the currently more widely used non-negativity, and that it can be enforced easily using quadratic cone programming. We demonstrate its use in a multi-tissue deconvolution framework that models the different tissue types in the continuous SHORE basis and can therefore be applied to data with multiple b values that are not organized on shells, such as in Diffusion Spectrum Imaging. Experiments on simulated fiber crossings, data from the Human Connectome Project, and clinical data, demonstrate the improved speed and accuracy of this new method.

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Bibtex

@INPROCEEDINGS{ankele:miccai16,
author = {Ankele, Michael and Lim, Lek-Heng and Gr{\"o}schel, Samuel and Schultz, Thomas},
title = {Fast and Accurate Multi-Tissue Deconvolution Using SHORE and H-psd Tensors},
booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
year = {2016},
publisher = {Springer},
note = {In press.},
abstract = {We propose a new regularization for spherical deconvolution in diffusion MRI. It is based on
observing that higher-order tensor representations of fiber ODFs should be H-psd, i.e., they should
have a positive semidefinite (psd) matrix H_T. We show that this constraint is stricter than the
currently more widely used non-negativity, and that it can be enforced easily using quadratic cone
programming. We demonstrate its use in a multi-tissue deconvolution framework that models the
different tissue types in the continuous SHORE basis and can therefore be applied to data with
multiple b values that are not organized on shells, such as in Diffusion Spectrum Imaging.
Experiments on simulated fiber crossings, data from the Human Connectome Project, and clinical data,
demonstrate the improved speed and accuracy of this new method.}
}